Using Generative Models to Improve Generative Models
Yubei CHEN, Assistant Professor, UC Davis

Training data plays a pivotal role in defining a model’s knowledge base. To effectively scale AI, we require a versatile “data simulator” capable of producing knowledge specific to any task. As we progress towards creating an all-encompassing world model, an intermediate step involves the strategic combination of various generative models. Each of these models replicates different aspects of reality, and together they can generate versatile data in concert. In this presentation, we will showcase a method for integrating multiple generative models, utilizing their basic strengths to generate sophisticated data. This technique not only refines the generative models themselves but also offers a solution to the issue of data scarcity in numerous edge AI applications. This marks an important step towards the future of data-centric efficient AI.